The present invention relates to image processing systems and methods, and more particularly to digital systems and methods.
A wide variety of image processing systems have been developed enabling digital computers to "see" or "read" an image. Typically, these image processors include a video camera, an analog-to-digital converter for digitizing the video signal produced by the camera, and a digital device for processing the digitized information. Typically, the image is digitized into a matrix or lattice of pixels with 512 pixels for each video scan line. These image processors using a camera as an "eye", an analog-to-digital converter as an "optic nerve", and a digital computing device as a "brain" are capable of scanning digital images and processing the digital information to interpret the image.
One relatively efficient image processor, known as a cytocomputer and developed by the Environmental Research Institute of Michigan (ERIM) located in Ann Arbor, Mich., utilizes "neighborhood theory" and "mathematical morphology" to manipulate a digital image. Disclosures of this system are provided in U.S. Pat. No. 4,369,430, entitled IMAGE ANALYZER WITH CYCLICAL NEIGHBORHOOD PROCESSING PIPELINE, issued Jan. 18, 1983, To Sternberg; U.S. Pat. No. 4,322,716, entitled METHOD AND APPARATUS FOR PATTERN RECOGNITION AND DETECTION, issued Mar. 30, 1982, to Sternberg; U.S. Pat. No. 4,301,443, entitled BIT ENABLE CIRCUITRY FOR AN IMAGE ANALYZER SYSTEM, issued Nov. 17, 1981, to Sternberg et al; U.S. Pat. No. 4,290,049, entitled DYNAMIC DATA CORRECTION GENERATOR FOR AN IMAGE ANALYZER SYSTEM, issued Sept. 15, 1981, To sternberg et al; U.S. Pat. No. 4,174,514, entitled PARALLEL PARTITIONED SERIAL NEIGHBORHOOD PROCESSORS, issued Nov. 13, 1979, to Sternberg; and U.S. Pat. No. 4,167,728, entitled AUTOMATIC IMAGE PROCESSOR, issued Sept. 11, 1979, to Sternberg; and in an article entitled "Biomedical Image Processing" by Sternberg, published in the January 1983 issue of Computer at pages 22-34. By routing the image sequentially through several neighborhood transformations, the computer is able to detect image features which are necessary to control a process, such as manufacturing or material handling. At each transformation stage, the "neighborhood" of pixels surrounding a given pixel in one image are examined and the corresponding pixel in the new image is given a digital value which is a function of the neighborhood pixels in the old image. In a cytocomputer, all neighborhood pixels in an image are made available for processing by serially routing the digital image through one or more shift registers. As the image is shifted through the registers, the appropriate register locations are accessed to process a particular neighborhood.
Although differing from previous image processors, a cytocomputer relying on the neighborhood theory is not without its drawbacks. First, the entire neighborhood of a pixel must be made available and examined before the corresponding pixel in the new image can be given a value. This requires delay and excessively complicated circuitry to make the neighborhood pixels simultaneously available and to drive the function generator utilizing the appropriate neighborhood information. Second, the neighborhood processing theory provides an inefficient and cumbersome method of effecting image erosions and image dilations, the principal operations of the mathematical morphology.
Other image processing systems and methods are known and disclosed in the Disclosure Statement filed with this application. These systems and methods all share the primary disadvantages of the cytocomputer because their processing is restricted to transformations on neighborhoods.